Question 3
a. The average length of a walleye (a delicious type of fish) on a certain lake is 18 inches with a standard deviation of 2.5 inches. Jerry comes back from a fishing trip and says he caught a walleye that was over 24 inches long. If we assume that the lengths of walleyes are normally distributed, what is the probability of randomly catching a walleye that is longer than 24 inches?
Show your work.
b. The average height of all American males over 20 is 69.1 inches(just over 5 feet, 9 inches) with population standard deviation of 3.8 inches. Assuming heights are normally distributed, what is the probability of randomly selecting and American male over 20 that is less than 62 inches tall? Show your work.

We are given the function f(x, y) = 5x² + 8y² and the domain of a closed triangular region bounded by the lines y = x, y = 2x, and x + y = 6. We need to find the absolute maximum and minimum values of the function within this domain.

To find the absolute maximum and minimum, we evaluate the function f(x, y) at all critical points and endpoints within the given domain.

First, we find the critical points by taking the partial derivatives of f(x, y) with respect to x and y, and setting them equal to zero. Solving the resulting system of equations, we obtain the critical point (x, y).

Next, we evaluate the function f(x, y) at the vertices of the triangular region, which are the points where the boundary lines intersect.

Finally, we compare the values of f(x, y) at the critical points and vertices to determine the absolute maximum and minimum values within the domain.

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The importance of the pooled variance is that it allows for more accurate and reliable statistical inferences

1. Importance of pooled variance Pooled variance is a method used to estimate the variance of two independent populations with unknown variances, based on the combined samples of the two populations. Pooled variance is an essential tool used in hypothesis testing, specifically in the two-sample t-test. When using the t-test, the pooled variance helps to account for any differences in sample sizes, as well as any variance differences between the two samples, in order to give a more accurate estimation of the true variance of the populations. Therefore, the importance of the pooled variance is that it allows for more accurate and reliable statistical inferences to be made.

2. Is the F-distribution always positive or is it possible for it to be zero?

The F-distribution is a continuous probability distribution used in statistical inference. The F-distribution is always positive, as it represents the ratio of two positive variables. It cannot be zero as the denominator of the ratio (the denominator degrees of freedom) can never be zero.

3. Better ways to find the p-valuesP-values are calculated using statistical software or tables and represent the probability of observing a test statistic at least as extreme as the one observed, given the null hypothesis is true. To find p-values more accurately, one can use resampling methods like bootstrapping or permutation tests, which are computationally intensive but provide more accurate p-values. Another way to find more accurate p-values is to increase the sample size of the study, which increases the statistical power of the study, thereby decreasing the margin of error and producing more accurate p-values.

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The values of a₂ and a₃ are 7 and 13, respectively. The limit of the sequence is ∞. the sequence is defined by the recursive formula a₁ = 1 and an = (an-1 + 6) for n ≥ 2.

This means that the first term of the sequence is 1, and the subsequent terms are equal to the previous term plus 6.

To find a₂, we simply substitute n = 2 into the recursive formula. This gives us a₂ = (a₁ + 6) = (1 + 6) = 7.

To find a₃, we substitute n = 3 into the recursive formula. This gives us a₃ = (a₂ + 6) = (7 + 6) = 13.

We can see that the terms of the sequence are increasing at an increasing rate. This means that the sequence does not converge, and the limit of the sequence is ∞.

Here is a more detailed explanation of the sequence and its limit. The sequence is defined by the recursive formula a₁ = 1 and an = (an-1 + 6) for n ≥ 2. This means that the first term of the sequence is 1, and the subsequent terms are equal to the previous term plus 6.

The sequence is increasing because each term is greater than the previous term. The rate of increase is also increasing because the difference between each term and the previous term is also increasing. This means that the sequence does not converge, and the limit of the sequence is ∞.

The limit of a sequence is the value that the sequence approaches as n approaches infinity. In this case, the sequence does not converge, which means that the limit of the sequence does not exist.

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The population mean prescription co-pay was $19.50 and the population standard deviation was $12.75. After the new program is implemented, the average prescription co-pay for a sample of 55 patients was $15.25.

The margin of error and constructing the 90% confidence interval for the new population mean, assuming that the population standard deviation remains unchanged is done as follows; Construct a 90% confidence interval. Step 1: Find the standard errorσ/√n=12.75/√55

=1.71

Find the margin of error Since we need to construct a 90% confidence interval, the area of the central region is given as 90% + 10/2 = 95%

= 0.95.

z= 1.645

margin of error (E)= z × standard error

= 1.645 × 1.71

= 2.81

Calculate the confidence interval Lower Limit= x - E= 15.25 - 2.81

= 12.44

Upper Limit= x + E= 15.25 + 2.81

= 17.06 The 90% confidence interval is given as follows:12.44<μ< 17.06. Yes, the result seems to support the pharmacy's claim that the new program has reduced the average prescription co-pay. Since the calculated 90% confidence interval (12.44, 17.06) does not contain the previously known population mean of $19.50, it shows that there is a significant decrease in the population mean prescription co-pay. Hence, we can conclude that the new program has significantly reduced the average prescription co-pay.

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a) -3.707

b) -2.896

c) 2.567

To find the values in the t-distribution table, we need the degrees of freedom (df) and the desired significance level (α). Let's calculate the values using the provided information:

(a) To find t₀.₀₀₅ when df = 6:

We have df = 6 and α = 0.005. Referring to the t-distribution table, we look for the row corresponding to df = 6 and find the column that is closest to α = 0.005. The intersection of the row and column gives us the value t₀.₀₀₅.

Based on the table, t₀.₀₀₅ when df = 6 is approximately -3.707.

(b) To find t₀.₀₁ when df = 8:

We have df = 8 and α = 0.01. Using the t-distribution table, we locate the row for df = 8 and find the column closest to α = 0.01. The value at the intersection of the row and column is t₀.₀₁.

Based on the table, t₀.₀₁ when df = 8 is approximately -2.896.

(c) To find t₀.₀₉₇₅ when df = 17:

We have df = 17 and α = 0.975. Referring to the t-distribution table, we find the row corresponding to df = 17 and locate the column closest to α = 0.975. The value at the intersection of the row and column is t₀.₀₉₇₅.

Based on the table, t₀.₀₉₇₅ when df = 17 is approximately 2.567.

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Solution for Initial value problem is y = -y + ex, y(0) = 4 is y(x) = 4xe-x. To solve the given initial value problem, we can start by rearranging the equation y = -y + ex to isolate the y term on one side.

Adding y to both sides gives us 2y = ex, and dividing both sides by 2 gives y = 0.5ex. However, this is not the solution that satisfies the initial condition y(0) = 4. To find the correct solution, we can substitute the initial condition y(0) = 4 into the general solution. Plugging in x = 0 and y = 4 into y(x) = 0.5ex gives us 4 = 0.5e0, which simplifies to 4 = 0.5. This is not true, so we need to adjust our general solution.

The correct solution that satisfies the initial condition is y(x) = 4xe-x. By substituting y = 4 into the general solution, we find that 4 = 4e0, which is true. Therefore, the solution to the initial value problem y = -y + ex, y(0) = 4 is y(x) = 4xe-x. This equation represents the specific solution that satisfies both the differential equation and the initial condition.

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(a) The 90% confidence interval estimate of the mean amount of mercury in tuna sushi is (0.315 ppm, 0.890 ppm). It does not appear that there is too much mercury in tuna sushi.

To construct a 90% confidence interval estimate of the mean amount of mercury in tuna sushi, we use the given sample data. The sample mean of the mercury levels is 0.725 ppm, and the sample standard deviation is 0.404 ppm.

Using the appropriate formula, we calculate the margin of error, which is 0.285 ppm. This margin of error is used to determine the range of values within which the true population mean is likely to fall.

The 90% confidence interval estimate is calculated by subtracting the margin of error from the sample mean to obtain the lower bound and adding the margin of error to the sample mean to obtain the upper bound. In this case, the confidence interval estimate is (0.440 ppm, 1.010 ppm).

Based on this confidence interval, it does not appear that there is too much mercury in tuna sushi. The upper bound of the confidence interval (1.010 ppm) is below the guideline of 1 ppm.

This suggests that the mean amount of mercury in the population of tuna sushi is likely to be below the safety guideline. Additionally, at least one of the sample values is less than 1 ppm, indicating that there are samples within the dataset that meet the safety guideline.

Therefore, the correct answer is (A) No, because it is possible that the mean is not greater than 1 ppm. Also, at least one of the sample values is less than 1 ppm, so at least some of the fish are safe.

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Answer:

The probability of selecting a yellow pencil from a bag containing five green and four yellow pencils can be solved by using probability tree diagrams.

i) Probability tree diagram:

Here, the first event is the selection of the first pencil, which can either be yellow or green. The second event is the selection of the second pencil, which can also be either yellow or green. The diagram can be drawn as follows:

```

G Y

/ \ / \

G Y G Y

/ \ / \ / \ / \

G Y G Y G Y G Y

```

The probability of selecting a yellow pencil is represented by the branches leading to the Y node, and the probability of selecting a green pencil is represented by the branches leading to the G node.

ii) Probability of getting both counters chosen as yellow:

The probability of getting both counters chosen as yellow is the probability of selecting a yellow pencil on the first draw and a yellow pencil on the second draw. The probability of selecting a yellow pencil on the first draw is 4/9, and the probability of selecting a yellow pencil on the second draw is 3/8 (since there are now only 3 yellow pencils left in the bag). The probability of both events occurring is:

(4/9) x (3/8) = 1/6

Therefore, the probability of getting both counters chosen as yellow is 1/6.

iii) Probability of getting one green counter and one yellow counter are chosen:

The probability of getting one green counter and one yellow counter can be found by adding the probabilities of two possible outcomes:

1. The first pencil is green and the second pencil is yellow.

2. The first pencil is yellow and the second pencil is green.

The probability of the first outcome is (5/9) x (4/8) = 5/18, and the probability of the second outcome is (4/9) x (5/8) = 5/18.

Adding these probabilities, we get:

5/18 + 5/18 = 10/18 = 5/9

Therefore, the probability of getting one green counter and one yellow counter are chosen is 5/9.

Step-by-step explanation:

The derivative of the function f(x) = 2x³ - x + 1 is f'(x) = 6x² - 1. To find the derivative of the function f(x) = 2x³ - x + 1, we can use the power rule and the constant rule for differentiation.

The power rule states that the derivative of xⁿ is n * x^(n-1), and the constant rule states that the derivative of a constant multiplied by a function is equal to the constant multiplied by the derivative of the function.

Let's apply these rules step-by-step to find f'(x):

Step 1: Take the derivative of each term separately.

The derivative of 2x³ is 6x², applying the power rule.

The derivative of -x is -1, applying the constant rule.

The derivative of 1 is 0, as the derivative of a constant is always 0.

Step 2: Combine the derivatives.

f'(x) = 6x² - 1 + 0

Simplifying the expression, we have:

f'(x) = 6x² - 1

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The given differential equation is: dx^2/d^2y(x) + 3(dx/dy(x)) - 10y(x)

= 3e^(-5x)The general solution of the corresponding homogeneous ODE is: y_h(x)

= Ae^(-5x) + Be^(2x)To find a particular solution y_p(x), we assume that it takes the form: y_p(x)

= C*e^(-5x)Here, C is an arbitrary constant to be determined.

We know that y'_p(x)

= -5C*e^(-5x) and y''_p(x)

= 25C*e^(-5x) Substituting y_p(x), y'_p(x) and y''_p(x) into the differential equation, we get:LHS

= dx^2/d^2y(x) + 3(dx/dy(x)) - 10y(x) = 25C*e^(-5x) - 15C*e^(-5x) - 10C*e^(-5x)

= 0Hence, we get C

= -3/10.Substituting the value of C in the equation for y_p(x), we get:y_p(x)

= (-3/10)*e^(-5x)

= (-3/10)*e^(-5x)

= (-3/10)*exp(-5*x).

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1. the tip of the man’s shadow is moving at a rate of 19.25 ft/sec (approx)

2. The speed is 42π mph (approx)

Question 1:The given parameters are:

Height of the pole, h = 15.5 ft.

Height of the man, a = 6 ft.

The man is walking away from the pole at a speed of v = 3 ft/sec.

Distance between the man and the pole, x = 41 ft.

Let y be the length of the man’s shadow.

We are to find how fast the tip of his shadow is moving.

We know that the length of the shadow is proportional to the distance between the man and the pole.

So, we can say:

y/x = h/a

=> y = hx/a

Differentiating both sides with respect to time, we get:

dy/dt = (h/a) dx/dt

=> dy/dt = (15.5/6) (3) = 77/4 ft/sec

Therefore, the tip of the man’s shadow is moving at a rate of 77/4 ft/sec when he is 41 feet away from the pole.

Answer: 19.25 ft/sec (approx)

Question 2:

Given parameters are:

The rate of rotation of the searchlight, de/dt = 3(2π) = 6π radians/min.

Distance of the wall from the searchlight, d = 7 miles.

We need to find the speed of the dot of light produced by the searchlight on the wall when the angle between the beam and the line through the searchlight perpendicular to the wall is θ radians.

To solve the problem, we will use the formula: v = (de/dt) (d cosθ)

Where v is the required speed of the dot.

The value of de/dt is given as 6π radians/min.

The value of d is 7 miles.

The value of θ can be obtained from the given data as follows:

Since the searchlight rotates at a rate of 3 revolutions per minute, or 3 (2π) radians per minute, we have:

de/dt = 6π radians/min

We can set up a proportion to get the value of θ in radians, as follows:

de/dt = (2π/rev) (3 rev/min) = 6π radians/min

So, we have:θ = de/dt dt/dθ = (6π/1) (1/3) = 2π radians

Substituting the values of de/dt, d, and θ into the formula for v, we get:

v = (de/dt) (d cosθ) = (6π) (7 cos2π) = -42π mph

Answer: -42π mph (approx)

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The derivative of f(x) = sin(x³) is 3x² cos(x³). To find the derivative of f(x) = sin(x³), we use the chain rule, which states that if we have a function g(x) inside another function f(x), then the derivative of f(g(x)) with respect to x is given by:f'(g(x)) * g'(x)In our case, we have f(x) = sin(x³), and g(x) = x³. Therefore, we have:f(x) = sin(g(x))f'(x) = cos(g(x)) * g'(x)

To find g'(x), we use the power rule, which states that if g(x) = x^n, then g'(x) = nx^(n-1). In our case, g(x) = x³, so g'(x) = 3x².Now, we can substitute into the formula for f'(x):f'(x) = cos(g(x)) * g'(x) = cos(x³) * 3x² = 3x² cos(x³)Therefore, the derivative of f(x) = sin(x³) is 3x² cos(x³). The derivative of a function is a measure of how quickly the function is changing at any given point. It tells us the slope of the tangent line to the function at that point. The derivative of a function can be found using various rules, such as the power rule, product rule, quotient rule, and chain rule.In this problem, we are asked to find the derivative of f(x) = sin(x³). To do this, we use the chain rule, which tells us that if we have a function g(x) inside another function f(x), then the derivative of f(g(x)) with respect to x is given by:

f'(g(x)) * g'(x)

In our case, we have:

f(x) = sin(x³), and g(x) = x³.

Therefore, we have:

f(x) = sin(g(x))f'(x) = cos(g(x)) * g'(x)To find g'(x),

we use the power rule, which tells us that if g(x) = x^n, then g'(x) = nx^(n-1). In our case, g(x) = x³, so g'(x) = 3x².Now, we can substitute into the formula for:

f'(x):f'(x) = cos(g(x)) * g'(x) = cos(x³) * 3x² = 3x² cos(x³)

Therefore, the derivative of f(x) = sin(x³) is 3x² cos(x³).

The derivative of f(x) = sin(x³) is 3x² cos(x³). The chain rule was used to find the derivative, and we found that the derivative of sin(x³) with respect to x is equal to cos(x³) times 3x².

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The probability of the third candy being the first red candy is the same as the probability of picking a red candy on any given pick, which is given as 10%.

Let's calculate the probabilities step by step:

Probability of picking a brown candy:

Since the given percentages account for all the colors, the remaining percentage must represent the brown candies. The probability of picking a brown candy is 100% - (5% + 10% + 5% + 5%) = 75%.

Probability of picking a yellow or blue candy:

The probability of picking a yellow candy is given as 5% and the probability of picking a blue candy is also given as 5%. To find the probability of picking a yellow or blue candy, we sum up these individual probabilities: 5% + 5% = 10%.

Probability of not picking a green candy:

The probability of picking a green candy is given as 5%. To find the probability of not picking a green candy, we subtract this probability from 100%: 100% - 5% = 95%.

Probability of picking a striped candy:

No information is provided about the percentage of striped candies. Therefore, without additional data, we cannot determine the probability of picking a striped candy.

Probability of picking three brown candies:

Assuming each candy is picked independently and with replacement (meaning after picking one candy, it is placed back in the bag), the probability of picking a brown candy three times in a row is calculated by multiplying the probabilities: 0.75 * 0.75 * 0.75 = 0.421875 or approximately 0.422.

Probability of the third candy being the first red:

If the candies are chosen with replacement, each pick is independent of the previous ones. Therefore, the probability of the third candy being the first red candy is the same as the probability of picking a red candy on any given pick, which is given as 10%.

Please note that for the probability of striped candies, more information is needed to calculate it accurately.

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The value of the test statistic is -24.73.The test statistic is a measure of how far the sample results are from the hypothesized value. In this case, the hypothesized value is 98%, and the sample results are 88%.

The test statistic is negative because the sample results are less than the hypothesized value.

The value of the test statistic is -24.73. This is a very large value, and it indicates that the sample results are very unlikely to have occurred if the hypothesized value is true. This suggests that the null hypothesis is false, and that the claim that fewer than 98% of adults have a cell phone is probably true.

The test statistic is calculated using the following formula:

z = (p_hat - p_0) / sqrt(p_0 * (1 - p_0) / n)

where:

p_hat is the sample proportion

p_0 is the hypothesized proportion

n is the sample size

In this case, the values are:

p_hat = 0.88

p_0 = 0.98

n = 1199

Substituting these values into the formula, we get:

z = (0.88 - 0.98) / sqrt(0.98 * (1 - 0.98) / 1199) = -24.73

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To find the probability that the strategy of always switching succeeds, given that Monty opens door 3, we can follow a similar approach as in part (b).

Let's go step by step:

Define basic events:

E3: Monty opens door 3

Ci: the car is behind door i (where i = 1, 2, 3)

Extract probability information:

We know that Monty opens door 3, so P(E3 | C1) = 0 and P(E3 | C2) = 1. The probability that Monty opens door 2, P(E2 | C1), is given by p.

Reasoning:

In this scenario, we choose door 1, Monty opens door 3, and we switch to door 2. We win if and only if the car is behind door 2. Therefore, our winning possibility is P(C2 | E3).

Compute P(C2 | E3):

We can use Bayes' rule and the law of total probability to calculate P(C2 | E3):

P(C2 | E3) = P(E3 | C2) * P(C2) / P(E3)

Using the law of total probability:

P(E3) = P(E3 | C1) * P(C1) + P(E3 | C2) * P(C2) + P(E3 | C3) * P(C3)

Since P(E3 | C1) = 0 and P(E3 | C2) = 1, we can simplify the expression:

P(E3) = P(E3 | C2) * P(C2) + P(E3 | C3) * P(C3)

= P(C2) + P(E3 | C3) * P(C3)

Applying Bayes' rule:

P(C2 | E3) = P(E3 | C2) * P(C2) / (P(C2) + P(E3 | C3) * P(C3))

Since P(E3 | C2) = 1, we can further simplify:

P(C2 | E3) = P(C2) / (P(C2) + P(E3 | C3) * P(C3))

The probability that the car is behind door 2, P(C2), is initially 1/3, and the probability that Monty opens door 3, P(E3 | C3), is given by 1 - p.

Therefore, the probability that the strategy of always switching succeeds, given that Monty opens door 3, is:

P(C2 | E3) = (1/3) / ((1/3) + (1 - p) * (2/3))

= 1 / (1 + 2(1 - p))

Simplifying further, we get:

P(C2 | E3) = 1 / (1 + 2 - 2p)

= 1 / (3 - 2p)

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A value of n ≥ 16 ensures a significant difference in the number of heads flipped between the fair coin and the biased coin using the Chebyshev Bound with a desired confidence level of 95%.

To use the Chebyshev Bound to find a value of n that ensures a certain level of confidence in distinguishing between the fair coin and the biased coin, we need to determine the minimum number of coin flips required. The Chebyshev Bound states that for any random variable with a finite mean (μ) and variance (σ^2), the probability that the random variable deviates from its mean by more than k standard deviations is at most 1/k^2. In this case, we want to determine the minimum value of n such that the number of heads flipped deviates significantly from the expected value depending on the coin chosen. Let's assume the fair coin is chosen with a probability of 2/3, and the biased coin with a probability of 1/3.  For the fair coin, the expected number of heads flipped in n coin flips is n/2, and the variance is n/4.

For the biased coin, the expected number of heads flipped in n coin flips is (3/4) * n, and the variance is (3/16) * n. Now, we need to find a value of n such that the number of heads flipped is significantly different between the two coins. Let's set a desired confidence level of 95%. According to the Chebyshev Bound, we want the probability that the number of heads flipped deviates by more than k standard deviations to be at most 5%. Thus, we have: 1/k^2 ≤ 0.05. Solving for k, we find k ≥ 2. To ensure a significant difference between the two coins, we can choose a value of k equal to 2. For the fair coin: |n/2 - (3n/4)| ≥ 2 * √(n/4).  For the biased coin: |(3/4)n - (3n/4)| ≥ 2 * √((3/16)n). Simplifying these equations, we get: |n/4| ≥ √n ; |n/4| ≥ √(3/16)n. To find the minimum value of n that satisfies both inequalities, we can solve: n/4 ≥ √n ; n/4 ≥ √(3/16)n. Squaring both sides of the equations, we get: n^2/16 ≥ n ; n^2/16 ≥ (3/16)n. By comparing these equations, we can see that n ≥ 16 satisfies both inequalities. Therefore, we can conclude that a value of n ≥ 16 ensures a significant difference in the number of heads flipped between the fair coin and the biased coin using the Chebyshev Bound with a desired confidence level of 95%.

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The answer for the work needed to pump all the water is 981000π N·m, where π represents mathematical constant pi. This represents the total amount of energy required to lift the water to the desired level.

To find the work needed to pump all the water from the cylindrical tank to a level 1 m above the rim, we can use the concept of work as the product of force and distance. Here are the steps to solve it:

Given that the tank measures 6 m in height and contains water to a height of 2 m, the remaining 4 m of water needs to be pumped to a level 1 m above the rim.

The volume of the water to be pumped can be calculated using the formula for the volume of a cylinder: V = πr²h, where r is the radius and h is the height. In this case, the radius is 5 m and the height is 4 m.

The volume of water to be pumped is V = π * (5²) * 4 = 100π m³.

The weight of the water can be calculated using the specific weight of water, which is given as 9810 N/m³. The weight of the water is equal to the volume of water multiplied by the specific weight: W = (100π) * 9810 = 981000π N.

The work needed to pump the water can be calculated by multiplying the weight of the water by the distance it needs to be lifted. In this case, the water needs to be lifted 1 m above the rim.

The work required is W = 981000π * 1 = 981000π N·m.

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The number of steps required is 13 steps.

Here, we have,

given that,

The vertical height of a staircase is 7 feet, 10-1/4 inches (7'10-1/4"), and local codes require a MAXIMUM stair riser height of 7 inches.

now, we have,

Vertical height of staircase = 7 feet 10*1/4 inch

= ( 7*12 + 41/4 ) inch

= 94.25 inch

Maximum riser height = 7 inch

Number of Riser = Vertical height of staircase / maximum riser height.

= 94.25/7

= 13.46

Say 14

Number of steps = Number of Riser - 1

= 14-1

= 13 steps.

Hence, the number of steps required is 13 steps.

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It indicates the percentage increase or decrease in international sales for each year from 2013 to 2018 when compared to the international sales of the base period 2010-12.

The following information is taken from Johnson & Johnson's annual reports. Its common stock is listed on the New York Stock Exchange, using the symbol JNJ. Using the period 2010-12 as the base period, compute a simple index of international sales for each year from 2013 to 2018. (Round your answers to 1 decimal place.)

The table given below shows the simple index of international sales of Johnson & Johnson:YearSimple index of international sales 20132462014274120154282201650.4201754.1201859.1

The simple index of international sales of Johnson & Johnson for the year 2013 is 24.6. The calculation of the index is shown below: Simple index of international sales for 2013= ((International Sales for 2013)/(International Sales for 2010-2012))*100 = ((31,655)/(128,786))*100 = 24.6

Similar computations have been done for each year from 2013 to 2018 to obtain the corresponding simple index of international sales. The simple index of international sales of Johnson & Johnson has been computed by taking the period 2010-12 as the base period.

Therefore, it indicates the percentage increase or decrease in international sales for each year from 2013 to 2018 when compared to the international sales of the base period 2010-12.

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Scenario 1: This is a statistic because it is computed from a sample. The correct notation would be x bar. So the option b is correct.

Scenario 2: This is a parameter because it refers to a characteristic of the entire population. The correct notation would be p. So the option c is correct.

Scenario 1: A survey was carried out by the Huron Valley Humane Society by mailing a questionnaire to a sample of 20 families. They calculated how many pets on average each of these 20 families possessed.

In this instance, a computed value based on the sample represents the average number of pets owned by the 20 families. It serves as a representative of the sample and is employed to calculate the typical number of pets kept by Washtenaw County families.

It is a statistic because it was calculated using the sample data. x bar (pronounced 'x bar'), which denotes the sample mean, is the appropriate notation for the average number of pets owned by the 20 families. So the option b is correct.

Scenario 2: We are curious in the percentage of all Pioneer High School alumni who have taken AP statistics at Pioneer, according to the question.

In this case, the percentage of graduates from Pioneer High School who have taken AP statistics is the precise characteristic that is being discussed.

It is a parameter since the characteristic of the entire population is what we are interested in. When expressing the percentage of Pioneer High School alumni who have taken AP statistics, the correct notation is p, where p is the population proportion. So the option c is correct.

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At a 95% confidence level, the margin of error is approximately RM7.60.

We have,

To calculate the margin of error at a 95% confidence level, you can use the formula:

Margin of Error = Critical Value * Standard Error

Find the critical value corresponding to a 95% confidence level.

For a large sample size (n > 30), you can use the Z-score associated with a 95% confidence level, which is approximately 1.96.

Calculate the standard error using the formula:

Standard Error = Standard Deviation / √(Sample Size)

Given the sample information:

Sample Size (n) = 48

Sample Standard Deviation = RM27

Now, let's calculate the margin of error.

Standard Error = 27 / √48 ≈ 3.88 (rounded to two decimal places)

Margin of Error = 1.96 * 3.88 ≈ 7.60 (rounded to two decimal places)

Therefore,

At a 95% confidence level, the margin of error is approximately RM7.60.

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Given f(x)= 4x² +16x +12 x+3, let us consider the function f(x) has a removable discontinuity at x = -3. Let us factor the given equation 4x² +16x +12 x+3

We get 4(x+3)(x+1) / (x+3) => 4(x+1)at x = -3 the denominator becomes 0 but the numerator is not 0, so we can simplify the function by canceling the (x+3) term, then there will be no more discontinuity. f(x) has a removable discontinuity at x = -3. So, f(x) has a removable discontinuity at x = -3. A discontinuity in a function is a point where the function is not continuous. There are various types of discontinuities, but the three main types are removable, jump, and infinite discontinuities. Let us consider the given function:

f(x)= 4x² +16x +12 x+3.

To find whether the given function has removable discontinuity, jump discontinuity, or infinite discontinuity, we can analyze by factoring the given function. The given equation can be factored as:

4(x+3)(x+1) / (x+3) => 4(x+1).

When x=-3, the denominator becomes 0, which means there is a discontinuity. If the function can be simplified by canceling the (x+3) term, then there will be no more discontinuity. Hence, the function has a removable discontinuity at x=-3. The graph of f(x) will have a hole at x=-3.

Hence, f(x) has a removable discontinuity at x = -3, and the function can be simplified by canceling the (x+3) term.

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The task is to calculate the probability that the average weight (x) of a random sample of 45 does in a national park, in December, is less than 52 kg assuming a healthy population. Additionally, we need to compute the probability that x is less than 56.9 kg. Based on these probabilities and the average weight obtained from a sample of 45 does (x = 56.9 kg), we need to determine if the doe population is undernourished or not.

To calculate the probabilities, we can use the properties of the sampling distribution of the sample mean. Given that the population distribution is approximately normal with a mean (μ) of 55.0 kg and a standard deviation (σ) of 6.8 kg, the sampling distribution of the sample mean (x) will also be approximately normal.

For the first part, we need to find the probability that x < 52 kg. We can calculate this probability using the z-score formula:

Z = (x - μ) / (σ / sqrt(n))

where Z is the z-score, x is the sample mean, μ is the population mean, σ is the population standard deviation, and n is the sample size.

For the given values, Z = (52 - 55) / (6.8 / sqrt(45)) = -2.5. Using the z-table or a calculator, we can find that the probability corresponding to Z = -2.5 is approximately 0.0062.

For the second part, we need to calculate the probability that x < 56.9 kg. Using the same formula and substituting the values, we get Z = (56.9 - 55) / (6.8 / sqrt(45)) = 1.06. The corresponding probability for Z = 1.06 is approximately 0.8564.

Based on the calculated probabilities, if the average weight obtained from a sample of 45 does is 56.9 kg, the probability of observing such a value or a lower value is 0.8564, which is quite high. Therefore, it is unlikely that the doe population is undernourished.

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We are estimating the population proportion of people who have used cannabis within the past year. The parameter of interest in this scenario is the proportion of the entire population that has used cannabis.

Explanation:

To construct a confidence interval, we surveyed a random sample of 300 individuals and found that 72 of them reported using cannabis within the past year. This sample proportion, 72/300, gives us an estimate of the population proportion.

The confidence interval provides us with a range of values within which we can be reasonably confident that the true population proportion lies. A 95% confidence interval means that if we were to repeat this sampling process multiple times, we would expect the resulting intervals to capture the true population proportion in 95% of the cases.

By calculating the confidence interval, we can estimate the range of values for the population proportion with a certain level of confidence. This interval helps us understand the uncertainty associated with our estimate based on a sample, as it accounts for the variability that may arise from sampling variation.

It is important to note that the confidence interval does not provide an exact value for the population proportion. Instead, it gives us a range of plausible values based on our sample data. The wider the confidence interval, the more uncertain we are about the true population proportion. In this case, we can use the confidence interval to say, with 95% confidence, that the population proportion of people who have used cannabis within the past year lies within a certain range.

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A teacher can reply that it's hard to say what the probability is that it will take exactly 20 minutes to walk to school in the morning, as it will depend on various factors like traffic, weather conditions, and the teacher's walking speed. The teacher might tell the student that it would be more accurate to use a range of times and calculate the probability of arriving at school within that time range.

The calculation of probability for continuous random variables is different from the calculation of probability for discrete random variables. A continuous random variable is a variable that can take any value in a given range. For example, the time it takes to walk to school can take any value in the range between 0 and a certain maximum time.

Probability calculations for continuous random variables use probability density functions. A probability density function is a function that describes the probability of a continuous random variable falling within a given range. For example, if the probability density function for the time it takes to walk to school is a normal distribution,

we can use the parameters of the distribution to calculate the probability of arriving at school within a certain time range. For instance, we could use the mean and standard deviation of the distribution to find the probability that the teacher will arrive at school within 15 to 25 minutes.

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a) Σx = 431, Σy = 277, Σx² = 27475, Σy² = 11690, Σxy = 22210, and r ≈ 0.611.

c) Se ≈ 0.661, a ≈ -7.746, b ≈ 0.661, and x ≈ 71.833.

d) The predicted percentage of successful field goals (y) is 38.823%.

e) The 90% confidence interval for y when x = 75 is 35.065% to 42.581%.

(a) To find the required values, we'll calculate the sums and cross-products of the given data:

Σx = 63 + 61 + 75 + 86 + 73 + 73 = 431

Σy = 41 + 42 + 48 + 51 + 44 + 51 = 277

Σx² = 63² + 61² + 75² + 86² + 73² + 73² = 27475

Σy² = 41² + 42² + 48² + 51² + 44² + 51² = 11690

Σxy = 6341 + 6142 + 7548 + 8651 + 7344 + 7351 = 22210

To calculate the correlation coefficient (r), we'll use the formula:

r = (nΣxy - ΣxΣy) / √[(nΣx² - (Σx)²)(nΣy² - (Σy)²)]

r = (622210 - 431277) / √[(627475 - (431)²)(611690 - (277)²)]

≈ 0.611

Therefore, Σx = 431, Σy = 277, Σx² = 27475, Σy² = 11690, Σxy = 22210, and r ≈ 0.611.

(c) To find the approximate values, we'll use the formulas for the regression equation:

b = (nΣxy - ΣxΣy) / (nΣx² - (Σx)²)

a = (Σy - bΣx) / n

X = Σx / n

Substituting the values:

b = (622210 - 431277) / (6×27475 - (431)²)

≈ 0.661

a = (277 - 0.661×431) / 6

≈ -7.746

X = 431 / 6

≈ 71.833

Therefore, Se ≈ 0.661, a ≈ -7.746, b ≈ 0.661, and x ≈ 71.833.

(d) To find the predicted percentage y of successful field goals for x = 75%, we'll use the regression equation:

y = a + bx

Substituting the values:

y = -7.746 + 0.661 * 75

≈ 38.823

Therefore, the predicted percentage of successful field goals (ŷ) is 38.823%.

e) To find a 90% confidence interval for y when x = 75, we'll use the formula:

y ± t(Se√(1/n + (x - X)² / Σx²))

First, we need to find the critical t-value for a 90% confidence interval. Since the sample size is small (n = 6), we'll use the t-distribution with (n - 2) degrees of freedom.

t critical = t(0.05/2, n - 2)

= t(0.025, 4)

≈ 2.776

Substituting the values:

= 38.823 ± 2.776  (0.661√(1/6 + (75 - 71.833)² / 27475))

≈ 38.823 ± 3.758

Therefore, the 90% confidence interval for y when x = 75 is 35.065% to 42.581%.

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The Generalized Triangle Inequality states that for any real numbers a₁, a₂, ..., an, the sum of their absolute values is greater than or equal to the absolute value of their sum. This can be proven using the Principle of Mathematical Induction.

We will prove the Generalized Triangle Inequality using mathematical induction.

Base Case: For n = 2, the inequality reduces to |a₁ + a₂| ≤ |a₁| + |a₂|, which is the regular Triangle Inequality for two real numbers. This base case is true.

Inductive Step: Assume that the inequality holds for some positive integer k, i.e., for any a₁, a₂, ..., ak, we have |a₁ + a₂ + ... + ak| ≤ |a₁| + |a₂| + ... + |ak|.

We need to prove that the inequality holds for k + 1, i.e., for any a₁, a₂, ..., ak, ak+1, we have |a₁ + a₂ + ... + ak + ak+1| ≤ |a₁| + |a₂| + ... + |ak| + |ak+1|.

Using the Triangle Inequality for two numbers, we have:

|a₁ + a₂ + ... + ak + ak+1| ≤ |a₁ + a₂ + ... + ak| + |ak+1|

By the induction hypothesis, |a₁ + a₂ + ... + ak| ≤ |a₁| + |a₂| + ... + |ak|. Combining these inequalities, we get:

|a₁ + a₂ + ... + ak + ak+1| ≤ |a₁| + |a₂| + ... + |ak| + |ak+1|

This completes the proof by induction.

Therefore, we have proven that for any real numbers a₁, a₂, ..., an, the sum of their absolute values is greater than or equal to the absolute value of their sum, which is the Generalized Triangle Inequality.

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The 99% confidence interval for the proportion of people who prefer chocolate pie is (0.07, 0.15).

A magazine conducted a poll of 1000 adults who were asked to identify their favorite pie.

Among the 1000 respondents, 11% chose chocolate pie, and the margin of error was given as +4 percentage points.

The values of p, q, n, E, and p are given as follows:

Value of p:

the population proportion of the sample, which is 11%.Value of q: The complement of p, which is q = 1 - p. Hence, q = 1 - 0.11 = 0.89.

Value of n: the sample size, which is 1000.Value of E: the margin of error, which is given as +4 percentage points.

Hence, E = 4% or 0.04.

Value of α: It is a measure of how confident we are in our results. For a 99% confidence interval, α = 0.01.

Hence, a = 0.01.

To find the value of the z-score (zα/2), we use the normal distribution table for the standard normal variable Z.

Since the confidence interval is symmetrical, we take α/2 in each tail.α/2 = 0.01/2 = 0.005.

The area to the right of the z-score is 0.005 + 0.99 = 0.995. This corresponds to a z-score of 2.58 (approximately).

Now, we can use the formula of the confidence interval to find the lower and upper limits of the interval.

Lower limit = p - zα/2 * √(pq/n) = 0.11 - 2.58 * √[(0.11 * 0.89) / 1000] = 0.07

Upper limit = p + zα/2 * √(pq/n) = 0.11 + 2.58 * √[(0.11 * 0.89) / 1000] = 0.15

Hence, the 99% confidence interval for the proportion of people who the 99% confidence interval for the proportion of people who prefer chocolate pie is (0.07, 0.15).prefer chocolate pie is (0.07, 0.15).The value of α is 0.01.

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The random variable in this problem is the amount of mercury in the bass fish collected from the 53 different lakes in Florida. The population parameter is the average amount of mercury in all Florida lakes. The hypotheses are formulated to determine if there is enough evidence to show that the fish in all Florida lakes have different mercury levels than the allowable amount of 1mg/kg.

a. The symbol for the random variable involved in this problem is X, representing the amount of mercury in the bass fish collected from the 53 different lakes.

b. The wording for the random variable in context could be "the amount of mercury in the bass fish collected from each lake."

c. The symbol for the parameter involved in this problem is μ, representing the population mean of the amount of mercury in all Florida lakes.

d. The wording for the parameter in context is "the average amount of mercury in all Florida lakes."

To determine if the fish in all Florida lakes have different mercury levels than the allowable amount, we need to test the null hypothesis (H0) that the population mean (μ) is equal to or less than 1mg/kg against the alternative hypothesis (H1) that the population mean (μ) is greater than 1mg/kg. This can be represented as:

H0: μ ≤ 1

H1: μ > 1

By collecting data from the 53 different lakes and analyzing the amount of mercury in the bass fish, statistical tests can be performed to assess whether the data provide enough evidence to reject the null hypothesis in favor of the alternative hypothesis. The significance level of the test needs to be determined to evaluate the evidence and make a conclusion about the mercury levels in the fish from Florida lakes.

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We can calculate the probability that a person diagnosed with a cough is infected with COVID-19. The probability is found to be approximately 0.0025 or 0.25%.

To solve for the probability that a person diagnosed with a cough is infected with COVID-19, we can use Bayes' theorem. Let's denote A as the event of being infected with COVID-19, and B as the event of having a cough. We are interested in finding P(A|B), the probability of being infected with COVID-19 given that the person has a cough.

According to Bayes' theorem:

P(A|B) = (P(B|A) * P(A)) / P(B)

P(B|A) is the probability of having a cough given that a person is infected with COVID-19, which is stated as 50% or 0.5.

P(A) is the prior probability of being infected with COVID-19, which is given as 1/200 or 0.005.

P(B) is the probability of having a cough, which can be calculated using the law of total probability:

P(B) = P(B|A) * P(A) + P(B|not A) * P(not A)

= (0.5 * 0.005) + (0.2 * 0.995)

= 0.0025 + 0.199

= 0.2015

Plugging these values into Bayes' theorem:

P(A|B) = (0.5 * 0.005) / 0.2015

= 0.0025 / 0.2015

≈ 0.0124 or 0.25%

Therefore, the probability that a person diagnosed with a cough is infected with COVID-19 is approximately 0.0025 or 0.25%.

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